| PHUSE US |
2026 |
From Chaos to Clarity: Can AI/ML Truly Decode Unstructured Clinical Data? |
Umamaheswari Adiseshan |
source
presentation source |
4 |
| WUSS |
2025 |
Evolving Human Emotions Under a Global Crisis: An Analysis of Popular Music Sentiment |
Hamed Hemmati,
Bennett Frohock |
source |
23 |
| MWSUG |
2024 |
Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow |
Ryan Paul Lafler,
Anna T. K. Wade |
source |
18 |
| PHUSE EU |
2024 |
Topic and Sentiment Analysis of Anti-Vaccine Activism Using Natural Language Processing and Large Language Models |
Tamas Bosznay |
source
presentation source |
12 |
| SESUG |
2024 |
Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow |
Ryan Paul Lafler,
Anna T. K. Wade |
source |
18 |
| SESUG |
2024 |
Investigating the Role of Radiomics in PET Image Analysis for Improved T-Stage Tumor Staging |
Victor Aderanti |
source |
31 |
| SESUG |
2024 |
Unveiling the Diagnostic Potential of Radiomics-Based PET and CT Imaging through Statistical and Machine Learning Techniques |
Mostafa Zahed,
Janet Kireta,
Victor Aderanti,
Maryam Skafyan |
source |
21 |
| WUSS |
2024 |
Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow |
Ryan Paul Lafler,
Anna T. K. Wade |
source |
18 |
| WUSS |
2024 |
X-Ray Image Classification with Neural Networks |
Riley Rutan,
Eric C Braga,
Xinyu Du |
source |
10 |
| PharmaSUG CN |
2024 |
Application of Natural Language Processing with R in clinical trials |
Feifeng Zhou,
Zhenchao Chen |
source |
11 |
| PharmaSUG |
2024 |
Unraveling the Layers within Neural Networks: Designing Artificial and Convolutional Neural Networks for Classification and Regression Using Python-s Keras & TensorFlow |
Ryan Paul Lafler,
Anna T. K. Wade |
source |
18 |
| PHUSE EU |
2023 |
Exploring Antidepressant Side Effects in the UK Through Topic Modelling and Social Media Analysis |
Khalid Ismail,
Amna Dridi |
source
presentation source |
13 |
| WUSS |
2023 |
Unraveling the Layers within Neural Networks: Designing Artificial and Convolutional Neural Networks for Classification and Regression Tasks using TensorFlow in Python |
Ryan Paul Lafler,
Anna T. K. Wade |
source |
24 |
| SESUG |
2023 |
Unsupervised Dimension Reduction Techniques for Lung Cancer Diagnosis Based on Radiomics |
Janet Kireta,
Mostafa Zahed |
source |
21 |
| PharmaSUG |
2023 |
Exploring the use of AI-based image recognition and Machine Learning to Improve the Efficiency and Accuracy of TLFs Validation Process. |
Mor Ram-On,
Ilan Carmeli |
source |
5 |
| PharmaSUG |
2023 |
Revolutionizing Statistical Outputs Validation: a Product Demonstration of Verify, an ML-powered Automation Solution Streamlining the Validation Process |
Ilan Carmeli |
source |
11 |
| SESUG |
2022 |
Application of Feature Selection and Dimension Reduction Techniques on Large-Scale CT Dataset for Lung Cancer Diagnosis Based on Radiomics |
Mostafa Zahed,
Maryam Skafyan |
source |
22 |
| PHUSE EU |
2021 |
Studyathons: A New Way of Organising and Promoting Multidisciplinary Collaborations in Biomedical Open Science [presentation] |
Julia Kurps,
Maria Escala-Garcia |
source
video |
22 |
| PHUSE US |
2021 |
Regulatory Update & Townhall [presentation] |
Ram Iyer |
source |
26 |
| SGF |
2020 |
Apply the Key Ideas from Andrew Ng's Machine Learning Certification (Coursera) in SAS Viya |
Petri Roine |
source |
9 |
| SGF |
2020 |
Neural-Network Based Forecasting Strategies in SAS Viya |
Steven C. Mills |
source |
14 |
| SGF |
2020 |
Semi-automatic Feature Engineering from Transactional Data |
James A. Cox,
Biruk Gebremariam,
Tao Wang |
source |
17 |
| PHUSE EU |
2019 |
Using Image recognition in production support |
Thomas Dekelver |
source
presentation source |
8 |
| MWSUG |
2019 |
Classifying Risk in Life Insurance using Predictive Analytics |
Sai Gopi Krishna Govindarajula |
|
6 |
| SGF |
2018 |
Automated Assistants for Fraud Investigation Productivity |
Michael Ames |
source |
6 |
| SGF |
2018 |
Machine Learning Bots for Fraud, Waste, and Abuse |
Michael Ames |
source |
9 |
| SGF |
2018 |
Time Series Feature Extraction |
Michele A. Trovero,
Michael J. Leonard |
source |
18 |
| PHUSE EU |
2017 |
Topology-based Clinical Data Mining: Identifying Hidden Patterns in Clinical Datasets |
Sergey Glushakov |
source
presentation source |
18 |
| PharmaSUG |
2017 |
Topology-based Clinical Data Mining for Discovery of Hidden Patterns in Multidimensional Data |
Sergey Glushakov,
Iryna Kotenko,
Andrey Rekalo |
source |
20 |
| WUSS |
2016 |
Best Practices in Machine Learning Applications |
Brett Wujek |
source |
23 |
| SGF |
2016 |
A Multistage Modeling Strategy for Hierarchical Demand Forecasting |
Pu Wang |
source |
11 |
| SGF |
2016 |
Best Practices for Machine Learning Applications |
Brett Wujek,
Funda Güneş,
Patrick Hall |
source |
23 |
| SGF |
2015 |
Understanding Characteristics of Insider Threats by Using Feature Extraction |
Ila Gokarn,
Clifton Phua |
source |
14 |
| SGF |
2014 |
An Overview of Machine Learning with SAS Enterprise Miner |
Patrick Hall,
Jared Dean,
Ilknur Kaynar Kabul,
Jorge Silva |
source
zip |
24 |
| NESUG |
1993 |
The Application of SAS Procedures in the Evaluation of the Fractal Dimension as a Patteren Recognition Feature |
John S. DaPonte,
Joseph Vitale |
|
12 |
HOME
QUICK SEARCH
AUTHORS
KEYWORDS
HELP
BUGS